Basic level scene understanding: categories, attributes and structures
Author(s)Patterson, Genevieve; Xiao, Jianxiong; Hays, James; Russell, Bryan Christopher; Ehinger, Krista A; Torralba, Antonio; Oliva, Aude; ... Show more Show less
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A longstanding goal of computer vision is to build a system that can automatically understand a 3D scene from a single image. This requires extracting semantic concepts and 3D information from 2D images which can depict an enormous variety of environments that comprise our visual world. This paper summarizes our recent efforts toward these goals. First, we describe the richly annotated SUN database which is a collection of annotated images spanning 908 different scene categories with object, attribute, and geometric labels for many scenes. This database allows us to systematically study the space of scenes and to establish a benchmark for scene and object recognition. We augment the categorical SUN database with 102 scene attributes for every image and explore attribute recognition. Finally, we present an integrated system to extract the 3D structure of the scene and objects depicted in an image.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Frontiers in Psychology
Frontiers Media SA
Xiao, Jianxiong, James Hays, Bryan C. Russell, Genevieve Patterson, Krista A. Ehinger, Antonio Torralba, and Aude Oliva. “Basic Level Scene Understanding: Categories, Attributes and Structures.” Frontiers in Psychology 4 (2013).
Final published version
SUN database, basic level scene understanding, scene recognition, scene attributes, geometry recognition, 3D context